Classification of Skin Disease using Ensemble Data Mining Techniques

Abstract

Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid<br />development of technologies and the application of various data mining techniques in recent years, the progress of<br />dermatological predictive classification has become more and more predictive and accurate. Therefore, development of<br />machine learning techniques, which can effectively differentiate skin disease classification, is of vast importance. The<br />machine learning techniques applied to skin disease prediction so far, no techniques outperforms over all the others.<br />Methods: In this research paper, we present a new method, which applies five different data mining techniques and<br />then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We<br />use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an<br />ensemble machine learning method is applied. Results: The proposed ensemble method, which is based on machine<br />learning was tested on Dermatology datasets and classify the type of skin disease in six different classes like include<br />C1: psoriasis, C2: seborrheic dermatitis, C3: lichen planus, C4: pityriasis rosea, C5: chronic dermatitis, C6: pityriasis<br />rubra. The results show that the dermatological prediction accuracy of the test data set is increased compared to a single<br />classifier. Conclusion: The ensemble method used on Dermatology datasets give better performance as compared to<br />different classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.